A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space

被引：0

作者：

Shi W. ^{[1
]}

Zhuo J. ^{[1
]}

Lan Y. ^{[1
]}

机构：

[1] Shanghai Maritime University, Shanghai

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2019年 / 41卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Attribute similarity; Attribute topology subspace; Clustering reliability; Fuzzy C-Means (FCM) clustering; Principle of maximum attribute similarity;

D O I：

10.11999/JEITdzyxxxb-41-11-2722

中图分类号：

学科分类号：

摘要：

With the attribute feature information of the fuzzy membership matrix and cluster centers after the iteration not fully utilized, the results of Fuzzy C-Means (FCM) Clustering and related modified algorithms are determined based on the principle of maximum fuzzy membership, causing bad influence on the clustering accuracy. To solve this problem, the improvement ideas are proposed: to improve classification principle of FCM. The formula definition of attribute similarity in binary topological subspaces is given. Then, the improved FCM algorithm based on the Similarity of Attribute Space (FCM-SAS) is proposed: First, samples with fuzzy membership degree lower than the clustering reliability are selected as suspicious samples. Next, the attribute similarity between the suspicious samples and the cluster centers after clustering are calculated. Finally, cluster labels of suspicious samples based on the principle of maximum attribute similarity are updated. The validity and superiority of the proposed algorithm is verified by the UCI sample set experiments and comparisons with other modified algorithms based on the principle of maximum fuzzy membership. © 2019, Science Press. All right reserved.

引用

页码：2722 / 2728

页数：6

共 26 条

[1]

Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, pp. 155-201, (1981)

[2]

Frigui H., Krishnapuram R., A robust competitive clustering algorithm with applications in computer vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 5, pp. 450-465, (1999)

[3]

Yang M., Nataliani Y., Robust-learning fuzzy C-means clustering algorithm with unknown number of clusters, Pattern Recognition, 71, pp. 45-59, (2017)

[4]

Son L.H., Tien N.D., Tune up fuzzy C-means for big data: Some novel hybrid clustering algorithms based on initial selection and incremental clustering, International Journal of Fuzzy Systems, 19, 5, pp. 1585-1602, (2017)

[5]

Huang C., Chung F., Wang S., Generalized competitive agglomeration clustering algorithm, International Journal of Machine Learning and Cybernetics, 8, 6, pp. 1945-1969, (2017)

[6]

Singh C., Bala A., A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images, Applied Soft Computing, 68, pp. 447-457, (2018)

[7]

Saha A., Das S., Geometric divergence based fuzzy clustering with strong resilience to noise features, Pattern Recognition Letters, 79, pp. 60-67, (2016)

[8]

Xiao M., Xiao Z., Wen Z., Et al., Improved fcm clustering algorithm based on spatial correlation and membership smoothing, Journal of Electronics & Information Technology, 39, 5, pp. 1123-1129, (2017)

[9]

Wang X., Wang Y., Wang L., Improving fuzzy C-means clustering based on feature-weight learning, Pattern Recognition Letters, 25, 10, pp. 1123-1132, (2004)

[10]

Yang M.S., Nataliani Y., A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy, IEEE Transactions on Fuzzy Systems, 26, 2, pp. 817-835, (2018)

← 1 2 3 →